PolRoute-DS: um Dataset de Dados Criminais para Geração de Rotas de Patrulhamento Policial
Resumo
Esse artigo apresenta o dataset PolRoute-DS, criado para viabilizar o desenvolvimento e testes de abordagens de geração de rotas policiais em centros urbanos. O PolRoute-DS combina a estrutura espacial da cidade de interesse, representada como um grafo conexo e direcionado de segmentos de vias, com dados criminais obtidos de fontes públicas (no contexto deste artigo os dados são providos pela Secretaria da Segurança Pública de São Paulo). O PolRoute-DS se encontra disponível para uso da comunidade sob a licença Creative Commons By Attribution 4.0 International (versões CSV e PostgreSQL), e pode ser obtido em https://osf.io/mxrgu/.
Palavras-chave:
Policiamento ostensivo, rotas de patrulhamento, ciência de dados
Referências
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Saint-Guillain, M., Paquay, C., and Limbourg, S. (2021). Time-dependent stochastic vehicle routing problem with random requests: Application to online police patrol management in brussels. Eur. J. Oper. Res., 292(3):869–885.
Scabora, L. d. C., Spadon, G., Rodrigues, L. S., Cazzolato, M. T., Araujo, M. V. d. S., Sousa, E. P. M. d., Traina, A. J. M., Rodrigues Junior, J. F., and Traina Junior, C. (2019). G-franc: a dataset of criminal activities mapped as a complex network in a relational dbms. In Brazilian Symposium on Databases - SBBD, Fortaleza. SBC.
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Spadon, G., Scabora, L. C., Oliveira, P. H., Araujo, M. V. S., Machado, B. B., de Sousa, E. P. M., Jr., C. T., and Jr., J. F. R. (2017). Behavioral characterization of criminality spread in cities. In Koumoutsakos, P., Lees, M., Krzhizhanovskaya, V. V., Dongarra, J. J., and Sloot, P. M. A., editors, International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, volume 108 of Procedia Computer Science, pages 2537–2541. Elsevier.
Yoo, J. S. (2019). Crime data warehousing and crime pattern discovery. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems, DATA ’19, New York, NY, USA. Association for Computing Machinery.
Caban, J. J. and Gotz, D. (2015). Visual analytics in healthcare–opportunities and research challenges.
Dewinter, M., Vandeviver, C., Vander Beken, T., and Witlox, F. (2020). Analysing the police patrol routing problem: A review. ISPRS International Journal of Geo-Information, 9(3).
Inmon, W. H. (1996). The data warehouse and data mining. Commun. ACM, 39(11):49–50.
Lourenço, V., Mann, P., Guimaraes, A., Paes, A., and de Oliveira, D. (2018). Towards safer (smart) cities: Discovering urban crime patterns using logic-based relational machine learning. In 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, pages 1–8. IEEE.
Miranda, F., Doraiswamy, H., Lage, M., Zhao, K., Gonçalves, B., Wilson, L., Hsieh, M., and Silva, C. T. (2017). Urban pulse: Capturing the rhythm of cities. IEEE Trans. Vis. Comput. Graph., 23(1):791–800.
Miranda, F., Hosseini, M., Lage, M., Doraiswamy, H., Dove, G., and Silva, C. T. (2020). Urban mosaic: Visual exploration of streetscapes using large-scale image data. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM.
Ota, M., Vo, H. T., Silva, C. T., and Freire, J. (2017). Stars: Simulating taxi ride sharing at scale. IEEE Trans. Big Data, 3(3):349–361.
Reis, D., Melo, A., Coelho, A. L. V., and Furtado, V. (2006). Towards optimal police patrol routes with genetic algorithms. In Mehrotra, S., Zeng, D. D., Chen, H., Thuraisingham, B. M., and Wang, F., editors, Intelligence and Security Informatics, IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, USA, May 23-24, 2006, Proceedings, volume 3975 of Lecture Notes in Computer Science, pages 485–491. Springer.
Resende, M. G. C. and Ribeiro, C. C. (1997). A GRASP for graph planarization. Networks, 29(3):173–189.
Saint-Guillain, M., Paquay, C., and Limbourg, S. (2021). Time-dependent stochastic vehicle routing problem with random requests: Application to online police patrol management in brussels. Eur. J. Oper. Res., 292(3):869–885.
Scabora, L. d. C., Spadon, G., Rodrigues, L. S., Cazzolato, M. T., Araujo, M. V. d. S., Sousa, E. P. M. d., Traina, A. J. M., Rodrigues Junior, J. F., and Traina Junior, C. (2019). G-franc: a dataset of criminal activities mapped as a complex network in a relational dbms. In Brazilian Symposium on Databases - SBBD, Fortaleza. SBC.
Shapiro, J. M. (2006). Smart Cities: Quality of Life, Productivity, and the Growth Effects of Human Capital. The Review of Economics and Statistics, 88(2):324–335.
Spadon, G., Scabora, L. C., Oliveira, P. H., Araujo, M. V. S., Machado, B. B., de Sousa, E. P. M., Jr., C. T., and Jr., J. F. R. (2017). Behavioral characterization of criminality spread in cities. In Koumoutsakos, P., Lees, M., Krzhizhanovskaya, V. V., Dongarra, J. J., and Sloot, P. M. A., editors, International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, volume 108 of Procedia Computer Science, pages 2537–2541. Elsevier.
Yoo, J. S. (2019). Crime data warehousing and crime pattern discovery. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems, DATA ’19, New York, NY, USA. Association for Computing Machinery.
Publicado
04/10/2021
Como Citar
SÁ, Bruno Cunha; MULLER, Gustavo; BANNI, Maicon; SANTOS, Wagner; LAGE, Marcos; ROSSETI, Isabel; FROTA, Yuri; DE OLIVEIRA, Daniel.
PolRoute-DS: um Dataset de Dados Criminais para Geração de Rotas de Patrulhamento Policial. In: DATASET SHOWCASE WORKSHOP (DSW), 3. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 117-127.
DOI: https://doi.org/10.5753/dsw.2021.17420.
